Software applications written in a traditional manner have all the functionality defined within application code. Once such applications are deployed, the providers offering the applications have little leeway to affect the behavior of the application. The application may have a few parameters that can be adjusted, but generally the behavior of the application is fixed unless the application code is modified.
Service providers wishing to address dynamic markets need the flexibility to offer new services rapidly. The cycle time associated with modifying application code can slow the process down unacceptably. Therefore, there is a need for application code in which the behavior can be modified without waiting for a software release.
Similar motivations were involved in the development of the Intelligent Network and Advanced Intelligent Network concepts for constructing telephone network services. It was possible to build new services by changing the code within the telephone switch, but this was rather slow and cumbersome. The approach chosen was to precisely define the call model that specified the state of the telephone switch software in establishing a connection between two parties. At various points in the call model, the telephone switch could be configured to query an external service control point for instructions on how to proceed. New service functionality could be produced by changing the behavior of the logic in the service control point without making changes to the software within the switching system itself.
This approach is highly dependent on the common call model that specifies the state of the switching system and the expected behavior. Such a call model could be defined because the process of establishing calls is consistent from one system to another. Definition and agreement of the call model took a substantial amount of time, which was tolerable because the processing of telephone calls was relatively static over time. In environments where providers are offering new and varied services on a frequent basis, it will not be possible to define such a detailed model for the internal states of the service logic. While this common call model technique has desirable characteristics, it cannot be applied directly to the processing of emerging, new and varied services.
Telecommunications equipment vendors and operators addressed a similar problem in adding features to call processing systems. The agreed upon solution, the basis for the Advanced Intelligent Network (AIN), defines another common call model as the basis for processing the establishment of telephone calls. This common call model provides a pre-defined set of events where external systems can be queried for decisions. U.S. Pat. No. 5,940,487, “Programmable call processing system and method”, Bunch et al., for example, illustrates this approach on a distributed telecommunication switching system coupled to AIN. This separation of service switching points (switches), which process calls, and service control points, which process service logic, allows new calling services to be defined without making changes to the software within the switch. This approach worked well for controlling telephone calling in the circuit switched networks, and was extended into next-generation packet-switched networks through the results of industry forums such as the Parlay Group specifications and the Java Community Process JAIN specification. The fact that there is a need for such standardization is indicative of the limitations of this approach, in that it deals with a specific functionality (call control) and requires advance agreement and/or standardization to function. Thus this approach is appropriate for application functionality that is static over time.
In U.S. Pat. No. 6,970,901, “Device and method for swapping out a part of a service logic program”, Moritz teaches a mechanism for distributing service logic across multiple entities. Moritz specifically focuses on making use of the increasing capabilities of intelligent terminal devices acting as client terminals, by distributing a portion of the service logic to the client terminal. Moritz discloses methods for using this distributed approach for determining charging related information. Distribution to client devices can enable personalization, but does not easily enable modification of the behavior of an overall service, because the change would have to be propagated to all of the client terminal devices. Enabling distribution for a single topic, such as charging, is simplified because the communication mechanism (the “charge ticket” in Moritz) can be defined and coded in advance. Thus Moritz addresses the need for flexible service logic programs, but in a very limited domain.
In U.S. Pat. No. 6,967,957, “Architecture for the rapid creation of telephony services in a next generation network”, Anjum et al. describe an object-oriented call model, which “hides the detail of the underlying call-state management protocols and hardware from applications”. Specifically, Anjum et al. describe a new call model that is designed to be abstract enough to represent call control in both circuit-switched and packet switched telephony networks. Such a call model is more flexible than the Java Telephony Application Programming Interface (JTAPI) model on which it was based, but still represents a single functionality. Thus this model is not applicable in the more general case where the behaviors are not as well defined, or known clearly in advance.
The Policy Evaluation Enforcement Management (PEEM) effort within the Open Mobile Alliance (OMA), as specified in the draft requirements document “Policy Evaluation, Enforcement and Management Architecture” (OMA-AD-Policy_Evaluation_Enforcement_Management-V1—0-200600625-D) describes an architecture for policy-evaluation and execution as support for the OMA's service enablers. These enablers include functions such as group list management, messaging and location. The architecture is designed to provide a common framework for these enablers to query for a policy decision. Such a capability would be useful, although not required, as a building block for the policy-enabled programming described here, because it would simplify the process of mapping interfaces between systems. The OMA work, however, does not specify how an enabler would determine when to query a policy engine (the PEEM enabler), or which policy engine to query.
Another approach is that supported by workflow systems. In these systems, which are often applied to complex order processing, the set of processing steps is defined in textual fashion that is interpretted at run time, rather than compiled in the manner of software code. This provides flexibility, in that the workflow can be modified without changing the code of the workflow engine. However, the interpretation operation is inefficient, leading to performance that is adequate for order processing but generally insufficient for the execution of actual services.
Database systems provide yet another mechanism where triggers and stored procedures can be invoked to execute logic, providing a great deal of flexibility. However, the operations where the stored procedures can be executed exist only for database operations of insert, delete, update and select on the data in the database. The problem with this approach is the restriction for database operations; the entire specification is within the database system and not easily referenceable or modifiable unless one navigates through the data and the associated triggers and stored procedures.
Hence, current processes are restricted to specific types of software applications, e.g., call processing, and have required advanced agreement and/or standardization of the application processing model before they could be used. Other current processes exist only within middleware, such as databases or workflow systems, which limits their scope and does not meet performance requirements for service execution.
Thus there is a need for a system capable of determining dynamically when and where to query an external descision point, such as a policy engine. There is also a need for a structured and efficient mechanism for configuring such dynamic queries, characterized by breakpoints that can be configured to a controlled set of options.